System identification of a robot arm with extended Kalman filter and artificial neural networks

References

[1] L. Alkaiem and H. Sternberg, Analysis of inclination measurement by means of artificial neural networks – A comparison of static and dynamic networks, in: 3rd Joint International Symposium on Deformation Monitoring, Vienna, 2016.Search in Google Scholar

[2] L. Beyer, Genauigkeitssteigerung von Industrierobotern: insbesondere mit Parallelkinematik, Ph. D. thesis, Shaker, 2005.10.3139/104.100873Search in Google Scholar

[3] V. Cherkassky and F. Mulier, Learning from data: concepts, theory, and methods, Wiley-Interscience, 1998.Search in Google Scholar

[4] J. Dupuis, C. Holst and H. Kuhlmann, Improving the kinematic calibration of a coordinate measuring arm using configuration analysis, Precision Engineering 50 (2017), 171–182 (en).10.1016/j.precisioneng.2017.05.004Search in Google Scholar

[5] S. S. Haykin, Neural Networks – A Comprehensive Foundation, 2nd edition ed, Prentice Hall, 1999 (English).Search in Google Scholar

[6] S. S. Haykin, Kalman filtering and neural networks, Wiley, New York, 2001 (English).10.1002/0471221546Search in Google Scholar

[7] M. Heinert and W. Niemeier, From fully automated observations to a neural network model inference: The Bridge Fallersleben Gate in Brunswick, Germany 1999–2006, Journal of Applied Geodesy 1 (2007).10.1515/JAG.2007.010Search in Google Scholar

[8] C. Herrmann, M. Hennes, M. Juretzko, C. Munzinger and M. Schneider, Positioning and synchronization of industrial robots, in: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 15–17, 2010.10.1109/IPIN.2010.5647715Search in Google Scholar

[9] O. Heunecke, H. Kuhlmann, W. Welsch, A. Eichhorn and H. Neuner, Handbuch Ingenieurgeodäsie: Auswertung geodätischer Überwachungsmessungen, 2., neu bearbeitete und erweiterte auflage ed, Wichmann Verlag, 2013.Search in Google Scholar

[10] J. M. Hollerbach and C. W. Wampler, The calibration index and taxonomy for robot kinematic calibration methods, The international journal of robotics research 15 (1996), 573–591.10.1177/027836499601500604Search in Google Scholar

[11] International Organization for Standardization, ISO 9283:1998 Manipulating industrial robots – Performance criteria and related test methods, 1998.Search in Google Scholar

[12] K.-R. Koch, Robust estimations for the nonlinear Gauss Helmert model by the expectation maximization algorithm, Journal of Geodesy 88 (2014), 263–271 (en).10.1007/s00190-013-0681-9Search in Google Scholar

[13] R. Laufer, Prozedurale Qualitätsmodellierung und -management für Daten: ingenieurgeodätische verkehrstechnische Anwendungen., Verlag der Bayerischen Akademie der Wissenschaften, München, 2011 (English).Search in Google Scholar

[14] D. W. Marquardt, An Algorithm for Least-Squares Estimation of Nonlinear Parameters, SIAM Journal on Applied Mathematics 11 (1963), 431–441.10.1137/0111030Search in Google Scholar

[15] H. Neuner, Model selection for system identification by means of artificial neural networks, Journal of Applied Geodesy 6 (2012), 117–124.10.1515/jag-2012-0004Search in Google Scholar

[16] H.-N. Nguyen, J. Zhou and H.-J. Kang, A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network, Neurocomputing 151 (2015), 996–1005 (en).10.1016/j.neucom.2014.03.085Search in Google Scholar

[17] A. Nubiola and I. A. Bonev, Absolute calibration of an ABB IRB 1600 robot using a laser tracker, Robotics and Computer-Integrated Manufacturing 29 (2013), 236–245 (en).10.1016/j.rcim.2012.06.004Search in Google Scholar

[18] A. Pope, Some Pitfalls to be avoided in the iterative adjustment of nonlinear problems, in: Proceedings of the 38th Annual Meeting American Society of Photogrammetry, Washingthon D.C., 1972.Search in Google Scholar

[19] G. V. Puskorius and L. A. Feldkamp, Decoupled extended Kalman filter training of feedforward layered networks, in: Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on, 1, pp. 771–777, IEEE, 1991.10.1109/IJCNN.1991.155276Search in Google Scholar

[20] A. Reiterer, U. Egly, T. Vicovac, E. Mai, S. Moafipoor, D. A. Grejner-Brzezinska and C. K. Toth, Application of artificial intelligence in Geodesy – A review of theoretical foundations and practical examples, Journal of Applied Geodesy 4 (2010).10.1515/jag.2010.020Search in Google Scholar

[21] B. Siciliano and O. Khatib (eds.), Springer handbook of robotics, Springer, Berlin, 2008 (eng).10.1007/978-3-540-30301-5Search in Google Scholar

[22] S. Singhal and L. Wu, Training multilayer perceptrons with the extended Kalman algorithm, in: Advances in neural information processing systems, pp. 133–140, 1989.Search in Google Scholar

[23] Y. Wu, A. Klimchik, S. Caro, B. Furet and A. Pashkevich, Geometric calibration of industrial robots using enhanced partial pose measurements and design of experiments, Robotics and Computer-Integrated Manufacturing 35 (2015), 151–168 (en).10.1016/j.rcim.2015.03.007Search in Google Scholar

[24] X. Zhong, J. Lewis and F. L. N-Nagy, Inverse robot calibration using artificial neural networks, Engineering Applications of Artificial Intelligence 9 (1996), 83–93 (en).10.1016/0952-1976(95)00069-0Search in Google Scholar